A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

• Published in: Systems (Basel) 2024-11, Vol.12 (11), p.494
• Main Authors: Yang, Yanpu, Zhang, Kai, Lei, Zijing
• Format: Article
• Language: English
• Subjects: Case studies; consensus reaching; Decision making; design decision-making; Design optimization; Fuzzy logic; Fuzzy sets; Green products; intuitionistic fuzzy sets; Linguistics; Literature reviews; Preferences; Product design; Product development; Set theory; Social networks; trust network
• ISSN: 2079-8954; 2079-8954
• DOI: 10.3390/systems12110494

In the process of product design decision-making (PDDM), decision-makers (DMs) conventionally engage in discussions to evaluate design alternatives. Achieving a consistent result is essential for selecting optimal product design schemes, as it helps eliminate preference conflicts. However, uncertainties and ambiguities, along with the interrelationships among DMs, make it challenging to attain an acceptable consensus level in PDDM. To address this issue, intuitionistic fuzzy sets (IFSs) are introduced to capture DMs’ preferences regarding product design schemes, and a trust network is integrated to analyze DMs’ interrelationships. A double hierarchy linguistic term set (LTS) is employed to assess DMs’ relationships, and an incomplete trust network is supplemented by leveraging the transitivity principle, thereby determining DMs’ weights. By establishing a consensus measurement model, DMs contributing less to consensus are identified, and consensus optimization is achieved through the modification of DMs’ preferences or the calibration of their trust relationships. A consensus reaching process (CRP) for PDDM is proposed, and the technique for order preference by similarity to ideal solution (TOPSIS) is utilized to rank product design schemes after consensus is reached. A case study involving the decision-making process for a specific household disinfection machine design illustrates the efficacy of our method in achieving consensus by integrating vague PDDM data.